import sys
!{sys.executable} --version
Python 3.7.4
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import plotly.express as px
dir(px)
['Constant', 'IdentityMap', 'NO_COLOR', 'Range', '__all__', '__builtins__', '__cached__', '__doc__', '__file__', '__loader__', '__name__', '__package__', '__path__', '__spec__', '_chart_types', '_core', '_doc', '_imshow', '_special_inputs', 'absolute_import', 'area', 'bar', 'bar_polar', 'box', 'choropleth', 'choropleth_mapbox', 'colors', 'data', 'defaults', 'density_contour', 'density_heatmap', 'density_mapbox', 'funnel', 'funnel_area', 'get_trendline_results', 'histogram', 'imshow', 'line', 'line_3d', 'line_geo', 'line_mapbox', 'line_polar', 'line_ternary', 'optional_imports', 'parallel_categories', 'parallel_coordinates', 'pd', 'pie', 'scatter', 'scatter_3d', 'scatter_geo', 'scatter_mapbox', 'scatter_matrix', 'scatter_polar', 'scatter_ternary', 'set_mapbox_access_token', 'strip', 'sunburst', 'timeline', 'treemap', 'violin']
!pip install plotly
import plotly
import plotly.graph_objs as go
from plotly import tools
from plotly.offline import init_notebook_mode, plot, iplot
dir(plotly)
['__version__', 'colors', 'data', 'graph_objects', 'graph_objs', 'io', 'offline', 'tools', 'utils']
print(plotly.__version__)
4.9.0
current_data = pd.read_csv('https://raw.githubusercontent.com/datasets/covid-19/master/data/countries-aggregated.csv')
current_data.head()
| Date | Country | Confirmed | Recovered | Deaths | |
|---|---|---|---|---|---|
| 0 | 2020-01-22 | Afghanistan | 0 | 0 | 0 |
| 1 | 2020-01-23 | Afghanistan | 0 | 0 | 0 |
| 2 | 2020-01-24 | Afghanistan | 0 | 0 | 0 |
| 3 | 2020-01-25 | Afghanistan | 0 | 0 | 0 |
| 4 | 2020-01-26 | Afghanistan | 0 | 0 | 0 |
'''current_data.to_csv('F:/Spatial Analysis/current_covid_29Sep_data')'''
type(current_data)
pandas.core.frame.DataFrame
to assign confirmed cases of each country on map and I want colorbar as well u have to asisgn color='Confirmed'
# Choropleth Map of the World
fig = px.choropleth(current_data,locations='Country',locationmode='country names',color='Confirmed',animation_frame='Date')
fig.update_layout(title='Choropleth Map of Confirmed Cases -till today',template="plotly_dark")
fig.show()
# Continent Map using Choropleth
fig = px.choropleth(current_data,locations='Country',locationmode='country names',color='Confirmed',animation_frame='Date',scope='europe')
fig.update_layout(title='Choropleth Map of Confirmed Cases - Europe till today',template="plotly_dark")
fig.show()
fig = px.choropleth(current_data,locations='Country',locationmode='country names',color='Confirmed',animation_frame='Date',scope='asia')
fig.update_layout(title='Choropleth Map of Confirmed Cases - Asia on 28-09-2020',template="plotly_dark")
fig.show()
fig = px.scatter_geo(current_data,locations='Country',locationmode='country names',color='Confirmed',size='Confirmed',hover_name="Country",animation_frame='Date',title='Spread over Time')
fig.update(layout_coloraxis_showscale=False,layout_template="plotly_dark")
fig.show()
fig = px.choropleth(current_data,locations='Country',locationmode='country names',color='Recovered',animation_frame='Date')
fig.update_layout(title='Choropleth Map of Recovered Cases -till today',template="plotly_dark")
fig.show()
fig = px.scatter_geo(current_data,locations='Country',locationmode='country names',color='Recovered',size='Recovered',hover_name="Country",animation_frame='Date',title='Recovery over Time')
fig.update(layout_coloraxis_showscale=False,layout_template="plotly_dark")
fig.show()
1.WE have to wash our hands
2.WE have to cover our face with fask
3.We dont have to touch our face
4.Mainntain Social Distancing
5.HOME Stay if you can
fig = px.choropleth(current_data,locations='Country',locationmode='country names',color='Deaths',animation_frame='Date')
fig.update_layout(title='Choropleth Map of Deaths -till today',template="plotly_dark")
fig.show()
worldometer = pd.read_csv('F:/Spatial Analysis/archive (1)/worldometer_data.csv')
worldometer.head()
| Country/Region | Continent | Population | TotalCases | NewCases | TotalDeaths | NewDeaths | TotalRecovered | NewRecovered | ActiveCases | Serious,Critical | Tot Cases/1M pop | Deaths/1M pop | TotalTests | Tests/1M pop | WHO Region | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | USA | North America | 3.311981e+08 | 5032179 | NaN | 162804.0 | NaN | 2576668.0 | NaN | 2292707.0 | 18296.0 | 15194.0 | 492.0 | 63139605.0 | 190640.0 | Americas |
| 1 | Brazil | South America | 2.127107e+08 | 2917562 | NaN | 98644.0 | NaN | 2047660.0 | NaN | 771258.0 | 8318.0 | 13716.0 | 464.0 | 13206188.0 | 62085.0 | Americas |
| 2 | India | Asia | 1.381345e+09 | 2025409 | NaN | 41638.0 | NaN | 1377384.0 | NaN | 606387.0 | 8944.0 | 1466.0 | 30.0 | 22149351.0 | 16035.0 | South-EastAsia |
| 3 | Russia | Europe | 1.459409e+08 | 871894 | NaN | 14606.0 | NaN | 676357.0 | NaN | 180931.0 | 2300.0 | 5974.0 | 100.0 | 29716907.0 | 203623.0 | Europe |
| 4 | South Africa | Africa | 5.938157e+07 | 538184 | NaN | 9604.0 | NaN | 387316.0 | NaN | 141264.0 | 539.0 | 9063.0 | 162.0 | 3149807.0 | 53044.0 | Africa |
worldometer_new.style.background_gradient(cmap='RdPu')
| Country/Region | Continent | Population | TotalCases | TotalDeaths | TotalRecovered | ActiveCases | Serious,Critical | Tot Cases/1M pop | Deaths/1M pop | TotalTests | Tests/1M pop | WHO Region | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | USA | North America | 331198130.000000 | 5032179 | 162804.000000 | 2576668.000000 | 2292707.000000 | 18296.000000 | 15194.000000 | 492.000000 | 63139605.000000 | 190640.000000 | Americas |
| 1 | Brazil | South America | 212710692.000000 | 2917562 | 98644.000000 | 2047660.000000 | 771258.000000 | 8318.000000 | 13716.000000 | 464.000000 | 13206188.000000 | 62085.000000 | Americas |
| 2 | India | Asia | 1381344997.000000 | 2025409 | 41638.000000 | 1377384.000000 | 606387.000000 | 8944.000000 | 1466.000000 | 30.000000 | 22149351.000000 | 16035.000000 | South-EastAsia |
| 3 | Russia | Europe | 145940924.000000 | 871894 | 14606.000000 | 676357.000000 | 180931.000000 | 2300.000000 | 5974.000000 | 100.000000 | 29716907.000000 | 203623.000000 | Europe |
| 4 | South Africa | Africa | 59381566.000000 | 538184 | 9604.000000 | 387316.000000 | 141264.000000 | 539.000000 | 9063.000000 | 162.000000 | 3149807.000000 | 53044.000000 | Africa |
| 5 | Mexico | North America | 129066160.000000 | 462690 | 50517.000000 | 308848.000000 | 103325.000000 | 3987.000000 | 3585.000000 | 391.000000 | 1056915.000000 | 8189.000000 | Americas |
| 6 | Peru | South America | 33016319.000000 | 455409 | 20424.000000 | 310337.000000 | 124648.000000 | 1426.000000 | 13793.000000 | 619.000000 | 2493429.000000 | 75521.000000 | Americas |
| 7 | Chile | South America | 19132514.000000 | 366671 | 9889.000000 | 340168.000000 | 16614.000000 | 1358.000000 | 19165.000000 | 517.000000 | 1760615.000000 | 92022.000000 | Americas |
| 8 | Colombia | South America | 50936262.000000 | 357710 | 11939.000000 | 192355.000000 | 153416.000000 | 1493.000000 | 7023.000000 | 234.000000 | 1801835.000000 | 35374.000000 | Americas |
| 9 | Spain | Europe | 46756648.000000 | 354530 | 28500.000000 | nan | nan | 617.000000 | 7582.000000 | 610.000000 | 7064329.000000 | 151087.000000 | Europe |
| 10 | Iran | Asia | 84097623.000000 | 320117 | 17976.000000 | 277463.000000 | 24678.000000 | 4156.000000 | 3806.000000 | 214.000000 | 2612763.000000 | 31068.000000 | EasternMediterranean |
| 11 | UK | Europe | 67922029.000000 | 308134 | 46413.000000 | nan | nan | 73.000000 | 4537.000000 | 683.000000 | 17515234.000000 | 257873.000000 | Europe |
| 12 | Saudi Arabia | Asia | 34865919.000000 | 284226 | 3055.000000 | 247089.000000 | 34082.000000 | 1915.000000 | 8152.000000 | 88.000000 | 3635705.000000 | 104277.000000 | EasternMediterranean |
| 13 | Pakistan | Asia | 221295851.000000 | 281863 | 6035.000000 | 256058.000000 | 19770.000000 | 809.000000 | 1274.000000 | 27.000000 | 2058872.000000 | 9304.000000 | EasternMediterranean |
| 14 | Bangladesh | Asia | 164851401.000000 | 249651 | 3306.000000 | 143824.000000 | 102521.000000 | nan | 1514.000000 | 20.000000 | 1225124.000000 | 7432.000000 | South-EastAsia |
| 15 | Italy | Europe | 60452568.000000 | 249204 | 35187.000000 | 201323.000000 | 12694.000000 | 42.000000 | 4122.000000 | 582.000000 | 7099713.000000 | 117443.000000 | Europe |
| 16 | Turkey | Asia | 84428331.000000 | 237265 | 5798.000000 | 220546.000000 | 10921.000000 | 580.000000 | 2810.000000 | 69.000000 | 5081802.000000 | 60191.000000 | Europe |
| 17 | Argentina | South America | 45236884.000000 | 228195 | 4251.000000 | 99852.000000 | 124092.000000 | 1150.000000 | 5044.000000 | 94.000000 | 794544.000000 | 17564.000000 | Americas |
| 18 | Germany | Europe | 83811260.000000 | 215210 | 9252.000000 | 196200.000000 | 9758.000000 | 236.000000 | 2568.000000 | 110.000000 | 8586648.000000 | 102452.000000 | Europe |
| 19 | France | Europe | 65288306.000000 | 195633 | 30312.000000 | 82460.000000 | 82861.000000 | 384.000000 | 2996.000000 | 464.000000 | 3992206.000000 | 61147.000000 | Europe |
| 20 | Iraq | Asia | 40306025.000000 | 140603 | 5161.000000 | 101025.000000 | 34417.000000 | 517.000000 | 3488.000000 | 128.000000 | 1092741.000000 | 27111.000000 | EasternMediterranean |
| 21 | Philippines | Asia | 109722719.000000 | 119460 | 2150.000000 | 66837.000000 | 50473.000000 | 239.000000 | 1089.000000 | 20.000000 | 1669996.000000 | 15220.000000 | WesternPacific |
| 22 | Indonesia | Asia | 273808365.000000 | 118753 | 5521.000000 | 75645.000000 | 37587.000000 | nan | 434.000000 | 20.000000 | 1633156.000000 | 5965.000000 | South-EastAsia |
| 23 | Canada | North America | 37775022.000000 | 118561 | 8966.000000 | 103106.000000 | 6489.000000 | 2263.000000 | 3139.000000 | 237.000000 | 4319172.000000 | 114339.000000 | Americas |
| 24 | Qatar | Asia | 2807805.000000 | 112092 | 178.000000 | 108831.000000 | 3083.000000 | 77.000000 | 39922.000000 | 63.000000 | 511000.000000 | 181993.000000 | EasternMediterranean |
| 25 | Kazakhstan | Asia | 18798667.000000 | 95942 | 1058.000000 | 68871.000000 | 26013.000000 | 221.000000 | 5104.000000 | 56.000000 | 2163713.000000 | 115099.000000 | Europe |
| 26 | Egypt | Africa | 102516525.000000 | 95006 | 4951.000000 | 48898.000000 | 41157.000000 | 41.000000 | 927.000000 | 48.000000 | 135000.000000 | 1317.000000 | EasternMediterranean |
| 27 | Ecuador | South America | 17668824.000000 | 90537 | 5877.000000 | 71318.000000 | 13342.000000 | 378.000000 | 5124.000000 | 333.000000 | 258582.000000 | 14635.000000 | Americas |
| 28 | Bolivia | South America | 11688459.000000 | 86423 | 3465.000000 | 27373.000000 | 55585.000000 | 71.000000 | 7394.000000 | 296.000000 | 183583.000000 | 15706.000000 | Americas |
| 29 | Sweden | Europe | 10105596.000000 | 81967 | 5766.000000 | nan | nan | 38.000000 | 8111.000000 | 571.000000 | 863315.000000 | 85429.000000 | Europe |
| 30 | Oman | Asia | 5118446.000000 | 80713 | 492.000000 | 70910.000000 | 9311.000000 | 177.000000 | 15769.000000 | 96.000000 | 309212.000000 | 60411.000000 | EasternMediterranean |
| 31 | Israel | Asia | 9197590.000000 | 79559 | 576.000000 | 53427.000000 | 25556.000000 | 358.000000 | 8650.000000 | 63.000000 | 1872453.000000 | 203581.000000 | Europe |
| 32 | Ukraine | Europe | 43705858.000000 | 76808 | 1819.000000 | 42524.000000 | 32465.000000 | 158.000000 | 1757.000000 | 42.000000 | 1116641.000000 | 25549.000000 | Europe |
| 33 | Dominican Republic | North America | 10858648.000000 | 76536 | 1246.000000 | 40539.000000 | 34751.000000 | 317.000000 | 7048.000000 | 115.000000 | 281926.000000 | 25963.000000 | Americas |
| 34 | Panama | North America | 4321282.000000 | 71418 | 1574.000000 | 45658.000000 | 24186.000000 | 161.000000 | 16527.000000 | 364.000000 | 240995.000000 | 55769.000000 | Americas |
| 35 | Belgium | Europe | 11594739.000000 | 71158 | 9859.000000 | 17661.000000 | 43638.000000 | 61.000000 | 6137.000000 | 850.000000 | 1767120.000000 | 152407.000000 | Europe |
| 36 | Kuwait | Asia | 4276658.000000 | 70045 | 469.000000 | 61610.000000 | 7966.000000 | 127.000000 | 16378.000000 | 110.000000 | 522200.000000 | 122105.000000 | EasternMediterranean |
| 37 | Belarus | Europe | 9449001.000000 | 68503 | 580.000000 | 63756.000000 | 4167.000000 | nan | 7250.000000 | 61.000000 | 1344303.000000 | 142269.000000 | Europe |
| 38 | UAE | Asia | 9902079.000000 | 61845 | 354.000000 | 55739.000000 | 5752.000000 | nan | 6246.000000 | 36.000000 | 5262658.000000 | 531470.000000 | EasternMediterranean |
| 39 | Romania | Europe | 19224023.000000 | 57895 | 2566.000000 | 28992.000000 | 26337.000000 | 458.000000 | 3012.000000 | 133.000000 | 1319369.000000 | 68631.000000 | Europe |
| 40 | Netherlands | Europe | 17138756.000000 | 56982 | 6153.000000 | nan | nan | 37.000000 | 3325.000000 | 359.000000 | 1079860.000000 | 63007.000000 | Europe |
| 41 | Singapore | Asia | 5854932.000000 | 54555 | 27.000000 | 48031.000000 | 6497.000000 | nan | 9318.000000 | 5.000000 | 1474372.000000 | 251817.000000 | WesternPacific |
| 42 | Guatemala | North America | 17946899.000000 | 54339 | 2119.000000 | 42070.000000 | 10150.000000 | 5.000000 | 3028.000000 | 118.000000 | 172712.000000 | 9624.000000 | Americas |
| 43 | Portugal | Europe | 10193593.000000 | 52061 | 1743.000000 | 37840.000000 | 12478.000000 | 42.000000 | 5107.000000 | 171.000000 | 1705474.000000 | 167308.000000 | Europe |
| 44 | Poland | Europe | 37842302.000000 | 49515 | 1774.000000 | 35642.000000 | 12099.000000 | 72.000000 | 1308.000000 | 47.000000 | 2374686.000000 | 62752.000000 | Europe |
| 45 | Nigeria | Africa | 206606300.000000 | 45244 | 930.000000 | 32430.000000 | 11884.000000 | 7.000000 | 219.000000 | 5.000000 | 306894.000000 | 1485.000000 | Africa |
| 46 | Honduras | North America | 9919704.000000 | 45098 | 1423.000000 | 6116.000000 | 37559.000000 | 52.000000 | 4546.000000 | 143.000000 | 109292.000000 | 11018.000000 | Americas |
| 47 | Bahrain | Asia | 1706669.000000 | 42889 | 156.000000 | 39945.000000 | 2788.000000 | 41.000000 | 25130.000000 | 91.000000 | 876700.000000 | 513691.000000 | EasternMediterranean |
| 48 | Japan | Asia | 126435859.000000 | 42263 | 1026.000000 | 28877.000000 | 12360.000000 | 115.000000 | 334.000000 | 8.000000 | 938739.000000 | 7425.000000 | WesternPacific |
| 49 | Armenia | Asia | 2963811.000000 | 39819 | 772.000000 | 31556.000000 | 7491.000000 | nan | 13435.000000 | 260.000000 | 171600.000000 | 57898.000000 | Europe |
| 50 | Ghana | Africa | 31133483.000000 | 39642 | 199.000000 | 36384.000000 | 3059.000000 | 7.000000 | 1273.000000 | 6.000000 | 405817.000000 | 13035.000000 | Africa |
| 51 | Kyrgyzstan | Asia | 6534479.000000 | 38659 | 1447.000000 | 30099.000000 | 7113.000000 | 24.000000 | 5916.000000 | 221.000000 | 267718.000000 | 40970.000000 | Europe |
| 52 | Afghanistan | Asia | 39009447.000000 | 36896 | 1298.000000 | 25840.000000 | 9758.000000 | 31.000000 | 946.000000 | 33.000000 | 90396.000000 | 2317.000000 | EasternMediterranean |
| 53 | Switzerland | Europe | 8660952.000000 | 36108 | 1985.000000 | 31600.000000 | 2523.000000 | 23.000000 | 4169.000000 | 229.000000 | 822764.000000 | 94997.000000 | Europe |
| 54 | Algeria | Africa | 43926079.000000 | 33626 | 1273.000000 | 23238.000000 | 9115.000000 | 57.000000 | 766.000000 | 29.000000 | nan | nan | Africa |
| 55 | Azerbaijan | Asia | 10148243.000000 | 33247 | 479.000000 | 29275.000000 | 3493.000000 | 66.000000 | 3276.000000 | 47.000000 | 766179.000000 | 75499.000000 | Europe |
| 56 | Morocco | Africa | 36953359.000000 | 29644 | 449.000000 | 20553.000000 | 8642.000000 | 31.000000 | 802.000000 | 12.000000 | 1383816.000000 | 37448.000000 | EasternMediterranean |
| 57 | Uzbekistan | Asia | 33516027.000000 | 28315 | 175.000000 | 19291.000000 | 8849.000000 | 228.000000 | 845.000000 | 5.000000 | 1377915.000000 | 41112.000000 | Europe |
| 58 | Serbia | Europe | 8733665.000000 | 27332 | 621.000000 | 14047.000000 | 12664.000000 | 120.000000 | 3129.000000 | 71.000000 | 723137.000000 | 82799.000000 | Europe |
| 59 | Moldova | Europe | 4032983.000000 | 26628 | 828.000000 | 18676.000000 | 7124.000000 | 362.000000 | 6603.000000 | 205.000000 | 128076.000000 | 31757.000000 | Europe |
| 60 | Ireland | Europe | 4943200.000000 | 26372 | 1768.000000 | 23364.000000 | 1240.000000 | 5.000000 | 5335.000000 | 358.000000 | 652917.000000 | 132084.000000 | Europe |
| 61 | Kenya | Africa | 53881160.000000 | 24411 | 399.000000 | 10444.000000 | 13568.000000 | 44.000000 | 453.000000 | 7.000000 | 335318.000000 | 6223.000000 | Africa |
| 62 | Venezuela | South America | 28427499.000000 | 22299 | 195.000000 | 12146.000000 | 9958.000000 | 42.000000 | 784.000000 | 7.000000 | 1567431.000000 | 55138.000000 | Americas |
| 63 | Nepal | Asia | 29186486.000000 | 21750 | 65.000000 | 15389.000000 | 6296.000000 | nan | 745.000000 | 2.000000 | 731977.000000 | 25079.000000 | South-EastAsia |
| 64 | Austria | Europe | 9011577.000000 | 21696 | 719.000000 | 19596.000000 | 1381.000000 | 25.000000 | 2408.000000 | 80.000000 | 937275.000000 | 104008.000000 | Europe |
| 65 | Costa Rica | North America | 5098730.000000 | 21070 | 200.000000 | 7038.000000 | 13832.000000 | 103.000000 | 4132.000000 | 39.000000 | 96110.000000 | 18850.000000 | Americas |
| 66 | Ethiopia | Africa | 115223736.000000 | 20900 | 365.000000 | 9027.000000 | 11508.000000 | 185.000000 | 181.000000 | 3.000000 | 468814.000000 | 4069.000000 | Africa |
| 67 | Australia | Australia/Oceania | 25528864.000000 | 19890 | 255.000000 | 10941.000000 | 8694.000000 | 52.000000 | 779.000000 | 10.000000 | 4631419.000000 | 181419.000000 | WesternPacific |
| 68 | El Salvador | North America | 6489514.000000 | 19126 | 513.000000 | 9236.000000 | 9377.000000 | 509.000000 | 2947.000000 | 79.000000 | 251271.000000 | 38720.000000 | Americas |
| 69 | Czechia | Europe | 10711019.000000 | 17731 | 390.000000 | 12320.000000 | 5021.000000 | 17.000000 | 1655.000000 | 36.000000 | 728670.000000 | 68030.000000 | Europe |
| 70 | Cameroon | Africa | 26606188.000000 | 17718 | 391.000000 | 15320.000000 | 2007.000000 | 30.000000 | 666.000000 | 15.000000 | 149000.000000 | 5600.000000 | Africa |
| 71 | Ivory Coast | Africa | 26437950.000000 | 16447 | 103.000000 | 12484.000000 | 3860.000000 | nan | 622.000000 | 4.000000 | 104584.000000 | 3956.000000 | Africa |
| 72 | S. Korea | Asia | 51273732.000000 | 14519 | 303.000000 | 13543.000000 | 673.000000 | 18.000000 | 283.000000 | 6.000000 | 1613652.000000 | 31471.000000 | WesternPacific |
| 73 | Denmark | Europe | 5794279.000000 | 14306 | 617.000000 | 12787.000000 | 902.000000 | 2.000000 | 2469.000000 | 106.000000 | 1654512.000000 | 285542.000000 | Europe |
| 74 | Palestine | Asia | 5112340.000000 | 13398 | 92.000000 | 6907.000000 | 6399.000000 | nan | 2621.000000 | 18.000000 | 200280.000000 | 39176.000000 | EasternMediterranean |
| 75 | Bosnia and Herzegovina | Europe | 3278650.000000 | 13396 | 384.000000 | 7042.000000 | 5970.000000 | nan | 4086.000000 | 117.000000 | 147021.000000 | 44842.000000 | Europe |
| 76 | Bulgaria | Europe | 6942854.000000 | 13014 | 435.000000 | 7374.000000 | 5205.000000 | 47.000000 | 1874.000000 | 63.000000 | 294087.000000 | 42358.000000 | Europe |
| 77 | Madagascar | Africa | 27755708.000000 | 12526 | 134.000000 | 10148.000000 | 2244.000000 | 88.000000 | 451.000000 | 5.000000 | 46301.000000 | 1668.000000 | Africa |
| 78 | Sudan | Africa | 43943536.000000 | 11780 | 763.000000 | 6194.000000 | 4823.000000 | nan | 268.000000 | 17.000000 | 401.000000 | 9.000000 | EasternMediterranean |
| 79 | North Macedonia | Europe | 2083365.000000 | 11399 | 517.000000 | 7480.000000 | 3402.000000 | 3.000000 | 5471.000000 | 248.000000 | 109946.000000 | 52773.000000 | Europe |
| 80 | Senegal | Africa | 16783877.000000 | 10715 | 223.000000 | 7101.000000 | 3391.000000 | 33.000000 | 638.000000 | 13.000000 | 114761.000000 | 6838.000000 | Africa |
| 81 | Norway | Europe | 5425471.000000 | 9468 | 256.000000 | 8857.000000 | 355.000000 | 3.000000 | 1745.000000 | 47.000000 | 472841.000000 | 87152.000000 | Europe |
| 82 | DRC | Africa | 89802183.000000 | 9309 | 215.000000 | 8048.000000 | 1046.000000 | nan | 104.000000 | 2.000000 | nan | nan | Africa |
| 83 | Malaysia | Asia | 32406372.000000 | 9038 | 125.000000 | 8713.000000 | 200.000000 | 2.000000 | 279.000000 | 4.000000 | 991333.000000 | 30591.000000 | WesternPacific |
| 84 | French Guiana | South America | 299385.000000 | 8127 | 47.000000 | 7240.000000 | 840.000000 | 23.000000 | 27146.000000 | 157.000000 | 41412.000000 | 138324.000000 | nan |
| 85 | Gabon | Africa | 2230563.000000 | 7787 | 51.000000 | 5609.000000 | 2127.000000 | 11.000000 | 3491.000000 | 23.000000 | 85369.000000 | 38272.000000 | Africa |
| 86 | Tajikistan | Asia | 9557468.000000 | 7665 | 62.000000 | 6443.000000 | 1160.000000 | nan | 802.000000 | 6.000000 | nan | nan | Europe |
| 87 | Guinea | Africa | 13164905.000000 | 7664 | 49.000000 | 6757.000000 | 858.000000 | 24.000000 | 582.000000 | 4.000000 | 14407.000000 | 1094.000000 | Africa |
| 88 | Haiti | North America | 11416103.000000 | 7544 | 171.000000 | 4832.000000 | 2541.000000 | nan | 661.000000 | 15.000000 | 18443.000000 | 1616.000000 | Americas |
| 89 | Finland | Europe | 5541604.000000 | 7532 | 331.000000 | 6980.000000 | 221.000000 | nan | 1359.000000 | 60.000000 | 389500.000000 | 70287.000000 | Europe |
| 90 | Zambia | Africa | 18430129.000000 | 7164 | 199.000000 | 5786.000000 | 1179.000000 | nan | 389.000000 | 11.000000 | 90307.000000 | 4900.000000 | Africa |
| 91 | Luxembourg | Europe | 626952.000000 | 7073 | 119.000000 | 5750.000000 | 1204.000000 | 9.000000 | 11282.000000 | 190.000000 | 623994.000000 | 995282.000000 | Europe |
| 92 | Mauritania | Africa | 4660728.000000 | 6444 | 157.000000 | 5291.000000 | 996.000000 | 3.000000 | 1383.000000 | 34.000000 | 57387.000000 | 12313.000000 | Africa |
| 93 | Paraguay | South America | 7141091.000000 | 6375 | 66.000000 | 4974.000000 | 1335.000000 | 23.000000 | 893.000000 | 9.000000 | 135277.000000 | 18943.000000 | Americas |
| 94 | Albania | Europe | 2877470.000000 | 6016 | 188.000000 | 3155.000000 | 2673.000000 | 23.000000 | 2091.000000 | 65.000000 | 38997.000000 | 13553.000000 | Europe |
| 95 | Lebanon | Asia | 6822220.000000 | 5672 | 70.000000 | 1974.000000 | 3628.000000 | 46.000000 | 831.000000 | 10.000000 | 345268.000000 | 50609.000000 | EasternMediterranean |
| 96 | Croatia | Europe | 4102577.000000 | 5404 | 155.000000 | 4688.000000 | 561.000000 | 7.000000 | 1317.000000 | 38.000000 | 125317.000000 | 30546.000000 | Europe |
| 97 | Djibouti | Africa | 989387.000000 | 5330 | 59.000000 | 5057.000000 | 214.000000 | nan | 5387.000000 | 60.000000 | 59909.000000 | 60552.000000 | EasternMediterranean |
| 98 | Greece | Europe | 10417673.000000 | 5123 | 210.000000 | 1374.000000 | 3539.000000 | 14.000000 | 492.000000 | 20.000000 | 619393.000000 | 59456.000000 | Europe |
| 99 | Libya | Africa | 6880353.000000 | 4879 | 107.000000 | 652.000000 | 4120.000000 | nan | 709.000000 | 16.000000 | 59699.000000 | 8677.000000 | EasternMediterranean |
| 100 | Equatorial Guinea | Africa | 1407001.000000 | 4821 | 83.000000 | 2182.000000 | 2556.000000 | nan | 3426.000000 | 59.000000 | 44356.000000 | 31525.000000 | Africa |
| 101 | Maldives | Asia | 541448.000000 | 4680 | 19.000000 | 2725.000000 | 1936.000000 | 12.000000 | 8643.000000 | 35.000000 | 85587.000000 | 158071.000000 | South-EastAsia |
| 102 | CAR | Africa | 4837752.000000 | 4620 | 59.000000 | 1641.000000 | 2920.000000 | 2.000000 | 955.000000 | 12.000000 | 29589.000000 | 6116.000000 | nan |
| 103 | Hungary | Europe | 9657785.000000 | 4597 | 600.000000 | 3463.000000 | 534.000000 | 8.000000 | 476.000000 | 62.000000 | 352546.000000 | 36504.000000 | Europe |
| 104 | Malawi | Africa | 19174839.000000 | 4491 | 137.000000 | 2137.000000 | 2217.000000 | 4.000000 | 234.000000 | 7.000000 | 33466.000000 | 1745.000000 | Africa |
| 105 | Zimbabwe | Africa | 14883803.000000 | 4339 | 84.000000 | 1264.000000 | 2991.000000 | nan | 292.000000 | 6.000000 | 140421.000000 | 9434.000000 | Africa |
| 106 | Nicaragua | North America | 6632263.000000 | 3902 | 123.000000 | 2913.000000 | 866.000000 | nan | 588.000000 | 19.000000 | nan | nan | Americas |
| 107 | Hong Kong | Asia | 7503041.000000 | 3850 | 46.000000 | 2458.000000 | 1346.000000 | 39.000000 | 513.000000 | 6.000000 | 692430.000000 | 92287.000000 | WesternPacific |
| 108 | Congo | Africa | 5530506.000000 | 3546 | 58.000000 | 1589.000000 | 1899.000000 | nan | 641.000000 | 10.000000 | nan | nan | Africa |
| 109 | Montenegro | Europe | 628074.000000 | 3480 | 60.000000 | 2178.000000 | 1242.000000 | nan | 5541.000000 | 96.000000 | 38427.000000 | 61182.000000 | Europe |
| 110 | Thailand | Asia | 69817894.000000 | 3330 | 58.000000 | 3148.000000 | 124.000000 | 1.000000 | 48.000000 | 0.800000 | 749213.000000 | 10731.000000 | South-EastAsia |
| 111 | Somalia | Africa | 15933012.000000 | 3227 | 93.000000 | 1728.000000 | 1406.000000 | 2.000000 | 203.000000 | 6.000000 | nan | nan | EasternMediterranean |
| 112 | Mayotte | Africa | 273419.000000 | 3042 | 39.000000 | 2738.000000 | 265.000000 | 2.000000 | 11126.000000 | 143.000000 | 13000.000000 | 47546.000000 | nan |
| 113 | Eswatini | Africa | 1161348.000000 | 2968 | 55.000000 | 1476.000000 | 1437.000000 | 5.000000 | 2556.000000 | 47.000000 | 20784.000000 | 17896.000000 | Africa |
| 114 | Sri Lanka | Asia | 21422362.000000 | 2839 | 11.000000 | 2541.000000 | 287.000000 | 1.000000 | 133.000000 | 0.500000 | 166737.000000 | 7783.000000 | South-EastAsia |
| 115 | Cuba | North America | 11325899.000000 | 2775 | 88.000000 | 2409.000000 | 278.000000 | 4.000000 | 245.000000 | 8.000000 | 285471.000000 | 25205.000000 | Americas |
| 116 | Cabo Verde | Africa | 556581.000000 | 2734 | 27.000000 | 2010.000000 | 697.000000 | nan | 4912.000000 | 49.000000 | 61633.000000 | 110735.000000 | Africa |
| 117 | Namibia | Africa | 2545264.000000 | 2652 | 15.000000 | 563.000000 | 2074.000000 | 24.000000 | 1042.000000 | 6.000000 | 29233.000000 | 11485.000000 | Africa |
| 118 | Mali | Africa | 20302901.000000 | 2552 | 124.000000 | 1954.000000 | 474.000000 | nan | 126.000000 | 6.000000 | 25152.000000 | 1239.000000 | Africa |
| 119 | Slovakia | Europe | 5459915.000000 | 2480 | 29.000000 | 1824.000000 | 627.000000 | 2.000000 | 454.000000 | 5.000000 | 272322.000000 | 49877.000000 | Europe |
| 120 | South Sudan | Africa | 11206572.000000 | 2450 | 47.000000 | 1175.000000 | 1228.000000 | nan | 219.000000 | 4.000000 | 12044.000000 | 1075.000000 | Africa |
| 121 | Slovenia | Europe | 2078968.000000 | 2223 | 125.000000 | 1909.000000 | 189.000000 | 2.000000 | 1069.000000 | 60.000000 | 135702.000000 | 65274.000000 | Europe |
| 122 | Lithuania | Europe | 2718121.000000 | 2171 | 81.000000 | 1656.000000 | 434.000000 | 6.000000 | 799.000000 | 30.000000 | 540784.000000 | 198955.000000 | Europe |
| 123 | Estonia | Europe | 1326627.000000 | 2124 | 63.000000 | 1954.000000 | 107.000000 | nan | 1601.000000 | 47.000000 | 122880.000000 | 92626.000000 | Europe |
| 124 | Mozambique | Africa | 31333962.000000 | 2120 | 15.000000 | 795.000000 | 1310.000000 | nan | 68.000000 | 0.500000 | 65151.000000 | 2079.000000 | Africa |
| 125 | Rwanda | Africa | 12981546.000000 | 2111 | 5.000000 | 1258.000000 | 848.000000 | nan | 163.000000 | 0.400000 | 286251.000000 | 22051.000000 | Africa |
| 126 | Suriname | South America | 587154.000000 | 2096 | 29.000000 | 1446.000000 | 621.000000 | 9.000000 | 3570.000000 | 49.000000 | 2785.000000 | 4743.000000 | Americas |
| 127 | Guinea-Bissau | Africa | 1972277.000000 | 2032 | 27.000000 | 944.000000 | 1061.000000 | 5.000000 | 1030.000000 | 14.000000 | 1500.000000 | 761.000000 | Africa |
| 128 | Benin | Africa | 12151976.000000 | 1936 | 38.000000 | 1600.000000 | 298.000000 | 1.000000 | 159.000000 | 3.000000 | 93677.000000 | 7709.000000 | Africa |
| 129 | Iceland | Europe | 341465.000000 | 1930 | 10.000000 | 1825.000000 | 95.000000 | nan | 5652.000000 | 29.000000 | 149693.000000 | 438385.000000 | Europe |
| 130 | Sierra Leone | Africa | 7992169.000000 | 1877 | 67.000000 | 1427.000000 | 383.000000 | nan | 235.000000 | 8.000000 | nan | nan | Africa |
| 131 | Yemen | Asia | 29886897.000000 | 1768 | 508.000000 | 898.000000 | 362.000000 | nan | 59.000000 | 17.000000 | 120.000000 | 4.000000 | EasternMediterranean |
| 132 | Tunisia | Africa | 11830801.000000 | 1642 | 51.000000 | 1241.000000 | 350.000000 | 9.000000 | 139.000000 | 4.000000 | 100298.000000 | 8478.000000 | EasternMediterranean |
| 133 | New Zealand | Australia/Oceania | 5002100.000000 | 1569 | 22.000000 | 1524.000000 | 23.000000 | nan | 314.000000 | 4.000000 | 486943.000000 | 97348.000000 | WesternPacific |
| 134 | Angola | Africa | 32956300.000000 | 1483 | 64.000000 | 520.000000 | 899.000000 | 20.000000 | 45.000000 | 2.000000 | 64747.000000 | 1965.000000 | Africa |
| 135 | Uruguay | South America | 3474956.000000 | 1318 | 37.000000 | 1079.000000 | 202.000000 | 2.000000 | 379.000000 | 11.000000 | 126956.000000 | 36535.000000 | Americas |
| 136 | Latvia | Europe | 1883936.000000 | 1275 | 32.000000 | 1070.000000 | 173.000000 | nan | 677.000000 | 17.000000 | 207909.000000 | 110359.000000 | Europe |
| 137 | Jordan | Asia | 10213138.000000 | 1232 | 11.000000 | 1171.000000 | 50.000000 | 3.000000 | 121.000000 | 1.000000 | 628745.000000 | 61562.000000 | EasternMediterranean |
| 138 | Liberia | Africa | 5068618.000000 | 1224 | 78.000000 | 705.000000 | 441.000000 | nan | 241.000000 | 15.000000 | nan | nan | Africa |
| 139 | Uganda | Africa | 45867852.000000 | 1223 | 5.000000 | 1102.000000 | 116.000000 | nan | 27.000000 | 0.100000 | 288367.000000 | 6287.000000 | Africa |
| 140 | Cyprus | Asia | 1208238.000000 | 1208 | 19.000000 | 856.000000 | 333.000000 | nan | 1000.000000 | 16.000000 | 216597.000000 | 179267.000000 | Europe |
| 141 | Georgia | Asia | 3988368.000000 | 1206 | 17.000000 | 987.000000 | 202.000000 | nan | 302.000000 | 4.000000 | 240473.000000 | 60294.000000 | Europe |
| 142 | Burkina Faso | Africa | 20954852.000000 | 1158 | 54.000000 | 961.000000 | 143.000000 | nan | 55.000000 | 3.000000 | nan | nan | Africa |
| 143 | Niger | Africa | 24281433.000000 | 1153 | 69.000000 | 1057.000000 | 27.000000 | nan | 47.000000 | 3.000000 | 9052.000000 | 373.000000 | Africa |
| 144 | Togo | Africa | 8296582.000000 | 1012 | 22.000000 | 697.000000 | 293.000000 | 2.000000 | 122.000000 | 3.000000 | 45767.000000 | 5516.000000 | Africa |
| 145 | Syria | Asia | 17539600.000000 | 999 | 48.000000 | 311.000000 | 640.000000 | nan | 57.000000 | 3.000000 | nan | nan | EasternMediterranean |
| 146 | Jamaica | North America | 2962478.000000 | 958 | 12.000000 | 745.000000 | 201.000000 | nan | 323.000000 | 4.000000 | 41840.000000 | 14123.000000 | Americas |
| 147 | Malta | Europe | 441663.000000 | 946 | 9.000000 | 670.000000 | 267.000000 | nan | 2142.000000 | 20.000000 | 136713.000000 | 309541.000000 | Europe |
| 148 | Andorra | Europe | 77278.000000 | 944 | 52.000000 | 828.000000 | 64.000000 | 1.000000 | 12216.000000 | 673.000000 | 3750.000000 | 48526.000000 | Europe |
| 149 | Chad | Africa | 16467965.000000 | 942 | 76.000000 | 838.000000 | 28.000000 | nan | 57.000000 | 5.000000 | nan | nan | Africa |
| 150 | Gambia | Africa | 2422754.000000 | 935 | 16.000000 | 136.000000 | 783.000000 | nan | 386.000000 | 7.000000 | 5183.000000 | 2139.000000 | Africa |
| 151 | Sao Tome and Principe | Africa | 219544.000000 | 878 | 15.000000 | 797.000000 | 66.000000 | nan | 3999.000000 | 68.000000 | 3079.000000 | 14025.000000 | Africa |
| 152 | Botswana | Africa | 2356075.000000 | 804 | 2.000000 | 63.000000 | 739.000000 | 1.000000 | 341.000000 | 0.800000 | 68423.000000 | 29041.000000 | Africa |
| 153 | Bahamas | North America | 393616.000000 | 761 | 14.000000 | 91.000000 | 656.000000 | 1.000000 | 1933.000000 | 36.000000 | 4814.000000 | 12230.000000 | Americas |
| 154 | Vietnam | Asia | 97425470.000000 | 747 | 10.000000 | 392.000000 | 345.000000 | nan | 8.000000 | 0.100000 | 482456.000000 | 4952.000000 | WesternPacific |
| 155 | Lesotho | Africa | 2143943.000000 | 742 | 23.000000 | 175.000000 | 544.000000 | nan | 346.000000 | 11.000000 | 8771.000000 | 4091.000000 | Africa |
| 156 | Diamond Princess | nan | nan | 712 | 13.000000 | 651.000000 | 48.000000 | 4.000000 | nan | nan | nan | nan | nan |
| 157 | San Marino | Europe | 33938.000000 | 699 | 42.000000 | 657.000000 | 0.000000 | nan | 20596.000000 | 1238.000000 | 6068.000000 | 178797.000000 | Europe |
| 158 | Réunion | Africa | 895952.000000 | 671 | 5.000000 | 592.000000 | 74.000000 | 3.000000 | 749.000000 | 6.000000 | 35419.000000 | 39532.000000 | nan |
| 159 | Channel Islands | Europe | 174022.000000 | 597 | 47.000000 | 533.000000 | 17.000000 | nan | 3431.000000 | 270.000000 | 30721.000000 | 176535.000000 | nan |
| 160 | Guyana | South America | 786936.000000 | 538 | 22.000000 | 189.000000 | 327.000000 | 2.000000 | 684.000000 | 28.000000 | 5165.000000 | 6563.000000 | Americas |
| 161 | Tanzania | Africa | 59886383.000000 | 509 | 21.000000 | 183.000000 | 305.000000 | 7.000000 | 8.000000 | 0.400000 | nan | nan | Africa |
| 162 | Taiwan | Asia | 23821199.000000 | 477 | 7.000000 | 443.000000 | 27.000000 | nan | 20.000000 | 0.300000 | 82737.000000 | 3473.000000 | WesternPacific |
| 163 | Comoros | Africa | 871326.000000 | 396 | 7.000000 | 340.000000 | 49.000000 | nan | 454.000000 | 8.000000 | nan | nan | Africa |
| 164 | Burundi | Africa | 11922216.000000 | 395 | 1.000000 | 304.000000 | 90.000000 | nan | 33.000000 | 0.080000 | 15614.000000 | 1310.000000 | Africa |
| 165 | Myanmar | Asia | 54446389.000000 | 357 | 6.000000 | 308.000000 | 43.000000 | nan | 7.000000 | 0.100000 | 122290.000000 | 2246.000000 | South-EastAsia |
| 166 | Mauritius | Africa | 1271985.000000 | 344 | 10.000000 | 334.000000 | 0.000000 | nan | 270.000000 | 8.000000 | 205285.000000 | 161389.000000 | Africa |
| 167 | Isle of Man | Europe | 85078.000000 | 336 | 24.000000 | 312.000000 | 0.000000 | nan | 3949.000000 | 282.000000 | 8627.000000 | 101401.000000 | nan |
| 168 | Mongolia | Asia | 3283344.000000 | 293 | nan | 260.000000 | 33.000000 | 1.000000 | 89.000000 | nan | 38334.000000 | 11675.000000 | WesternPacific |
| 169 | Eritrea | Africa | 3551175.000000 | 282 | nan | 225.000000 | 57.000000 | nan | 79.000000 | nan | nan | nan | Africa |
| 170 | Guadeloupe | North America | 400131.000000 | 279 | 14.000000 | 179.000000 | 86.000000 | nan | 697.000000 | 35.000000 | 18476.000000 | 46175.000000 | nan |
| 171 | Martinique | North America | 375235.000000 | 276 | 15.000000 | 98.000000 | 163.000000 | 1.000000 | 736.000000 | 40.000000 | 12227.000000 | 32585.000000 | nan |
| 172 | Faeroe Islands | Europe | 48882.000000 | 266 | nan | 192.000000 | 74.000000 | 1.000000 | 5442.000000 | nan | 43045.000000 | 880590.000000 | nan |
| 173 | Aruba | North America | 106812.000000 | 263 | 3.000000 | 114.000000 | 146.000000 | nan | 2462.000000 | 28.000000 | 14047.000000 | 131511.000000 | nan |
| 174 | Cambodia | Asia | 16741375.000000 | 243 | nan | 210.000000 | 33.000000 | 1.000000 | 15.000000 | nan | 67807.000000 | 4050.000000 | WesternPacific |
| 175 | Trinidad and Tobago | North America | 1399950.000000 | 210 | 8.000000 | 135.000000 | 67.000000 | nan | 150.000000 | 6.000000 | 9559.000000 | 6828.000000 | Americas |
| 176 | Cayman Islands | North America | 65798.000000 | 203 | 1.000000 | 202.000000 | 0.000000 | nan | 3085.000000 | 15.000000 | 31108.000000 | 472780.000000 | nan |
| 177 | Gibraltar | Europe | 33690.000000 | 190 | nan | 184.000000 | 6.000000 | nan | 5640.000000 | nan | 23063.000000 | 684565.000000 | nan |
| 178 | Papua New Guinea | Australia/Oceania | 8963009.000000 | 163 | 3.000000 | 53.000000 | 107.000000 | nan | 18.000000 | 0.300000 | 10808.000000 | 1206.000000 | WesternPacific |
| 179 | Sint Maarten | North America | 42924.000000 | 160 | 16.000000 | 64.000000 | 80.000000 | 3.000000 | 3728.000000 | 373.000000 | 1115.000000 | 25976.000000 | nan |
| 180 | Bermuda | North America | 62254.000000 | 157 | 9.000000 | 144.000000 | 4.000000 | nan | 2522.000000 | 145.000000 | 26352.000000 | 423298.000000 | Americas |
| 181 | Brunei | Asia | 437893.000000 | 141 | 3.000000 | 138.000000 | 0.000000 | nan | 322.000000 | 7.000000 | 41148.000000 | 93968.000000 | nan |
| 182 | Barbados | North America | 287411.000000 | 133 | 7.000000 | 100.000000 | 26.000000 | nan | 463.000000 | 24.000000 | 12233.000000 | 42563.000000 | Americas |
| 183 | Turks and Caicos | North America | 38768.000000 | 129 | 2.000000 | 39.000000 | 88.000000 | 3.000000 | 3327.000000 | 52.000000 | 1252.000000 | 32295.000000 | nan |
| 184 | Seychelles | Africa | 98408.000000 | 126 | nan | 124.000000 | 2.000000 | nan | 1280.000000 | nan | nan | nan | Africa |
| 185 | Monaco | Europe | 39270.000000 | 125 | 4.000000 | 105.000000 | 16.000000 | 2.000000 | 3183.000000 | 102.000000 | 38209.000000 | 972982.000000 | Europe |
| 186 | Bhutan | Asia | 772443.000000 | 105 | nan | 93.000000 | 12.000000 | nan | 136.000000 | nan | 54589.000000 | 70671.000000 | South-EastAsia |
| 187 | Antigua and Barbuda | North America | 98010.000000 | 92 | 3.000000 | 76.000000 | 13.000000 | 1.000000 | 939.000000 | 31.000000 | 1500.000000 | 15305.000000 | Americas |
| 188 | Liechtenstein | Europe | 38139.000000 | 89 | 1.000000 | 85.000000 | 3.000000 | nan | 2334.000000 | 26.000000 | 900.000000 | 23598.000000 | Europe |
| 189 | Belize | North America | 398312.000000 | 86 | 2.000000 | 31.000000 | 53.000000 | 2.000000 | 216.000000 | 5.000000 | 3679.000000 | 9236.000000 | Americas |
| 190 | French Polynesia | Australia/Oceania | 281072.000000 | 64 | nan | 62.000000 | 2.000000 | nan | 228.000000 | nan | 5849.000000 | 20810.000000 | nan |
| 191 | St. Vincent Grenadines | North America | 110976.000000 | 56 | nan | 46.000000 | 10.000000 | nan | 505.000000 | nan | 2447.000000 | 22050.000000 | nan |
| 192 | Saint Martin | North America | 38729.000000 | 53 | 3.000000 | 41.000000 | 9.000000 | 1.000000 | 1368.000000 | 77.000000 | 1183.000000 | 30546.000000 | nan |
| 193 | Macao | Asia | 650193.000000 | 46 | nan | 46.000000 | 0.000000 | nan | 71.000000 | nan | 4071.000000 | 6261.000000 | nan |
| 194 | Curaçao | North America | 164161.000000 | 31 | 1.000000 | 28.000000 | 2.000000 | nan | 189.000000 | 6.000000 | 1080.000000 | 6579.000000 | nan |
| 195 | Fiji | Australia/Oceania | 897095.000000 | 27 | 1.000000 | 18.000000 | 8.000000 | nan | 30.000000 | 1.000000 | 6693.000000 | 7461.000000 | WesternPacific |
| 196 | Saint Lucia | North America | 183712.000000 | 25 | nan | 24.000000 | 1.000000 | nan | 136.000000 | nan | 3895.000000 | 21202.000000 | Americas |
| 197 | Timor-Leste | Asia | 1320812.000000 | 25 | nan | 24.000000 | 1.000000 | nan | 19.000000 | nan | 4238.000000 | 3209.000000 | South-EastAsia |
| 198 | Grenada | North America | 112576.000000 | 24 | nan | 23.000000 | 1.000000 | nan | 213.000000 | nan | 6252.000000 | 55536.000000 | Americas |
| 199 | New Caledonia | Australia/Oceania | 285769.000000 | 22 | nan | 22.000000 | 0.000000 | nan | 77.000000 | nan | 11099.000000 | 38839.000000 | nan |
| 200 | Laos | Asia | 7285750.000000 | 20 | nan | 19.000000 | 1.000000 | nan | 3.000000 | nan | 29374.000000 | 4032.000000 | WesternPacific |
| 201 | Dominica | North America | 72004.000000 | 18 | nan | 18.000000 | 0.000000 | nan | 250.000000 | nan | 1005.000000 | 13958.000000 | Americas |
| 202 | Saint Kitts and Nevis | North America | 53237.000000 | 17 | nan | 16.000000 | 1.000000 | nan | 319.000000 | nan | 1146.000000 | 21526.000000 | Americas |
| 203 | Greenland | North America | 56780.000000 | 14 | nan | 14.000000 | 0.000000 | nan | 247.000000 | nan | 5977.000000 | 105266.000000 | Europe |
| 204 | Montserrat | North America | 4992.000000 | 13 | 1.000000 | 10.000000 | 2.000000 | nan | 2604.000000 | 200.000000 | 61.000000 | 12220.000000 | nan |
| 205 | Caribbean Netherlands | North America | 26247.000000 | 13 | nan | 7.000000 | 6.000000 | nan | 495.000000 | nan | 424.000000 | 16154.000000 | nan |
| 206 | Falkland Islands | South America | 3489.000000 | 13 | nan | 13.000000 | 0.000000 | nan | 3726.000000 | nan | 1816.000000 | 520493.000000 | nan |
| 207 | Vatican City | Europe | 801.000000 | 12 | nan | 12.000000 | 0.000000 | nan | 14981.000000 | nan | nan | nan | Europe |
| 208 | Western Sahara | Africa | 598682.000000 | 10 | 1.000000 | 8.000000 | 1.000000 | nan | 17.000000 | 2.000000 | nan | nan | Africa |
def plot(df,x,y,xaxis_label,yaxis_label,title):
fig = px.bar(worldometer.head(10), y=y,x=x,color='WHO Region')
fig.update_layout(title=title,xaxis_title=xaxis_label,yaxis_title=yaxis_label)
fig.show()
plot(worldometer.head(10),'Country/Region','Deaths/1M pop','Country','Deaths/Million','Comparison of Deaths/Million of 10 Most Affected Countries')
plot(worldometer.head(10),'Country/Region','Tests/1M pop','Country','Tests/M pop','Comparison of Tests/Million of 10 Most Affected Countries')
'''
fig = go.Figure()
fig.add_trace(go.Bar(x=worldometer['Country/Region'].head(10), y=worldometer['TotalTests'].head(10)))
fig.update_layout(
title="Plot Title",
xaxis_title="X Axis Title",
yaxis_title="X Axis Title")
fig.show()
'''
'\nfig = go.Figure()\nfig.add_trace(go.Bar(x=worldometer[\'Country/Region\'].head(10), y=worldometer[\'TotalTests\'].head(10)))\nfig.update_layout(\n title="Plot Title",\n xaxis_title="X Axis Title",\n yaxis_title="X Axis Title")\nfig.show()\n'
'''
fig = px.bar(worldometer.head(10), y='Deaths/1M pop',x='Country/Region',color='WHO Region',height=400)
fig.update_layout(title='Comparison of Deaths/Million of 10 Most Affected Countries',xaxis_title='Country',yaxis_title='Deaths/Million')
fig.show()'''
"\nfig = px.bar(worldometer.head(10), y='Deaths/1M pop',x='Country/Region',color='WHO Region',height=400)\nfig.update_layout(title='Comparison of Deaths/Million of 10 Most Affected Countries',xaxis_title='Country',yaxis_title='Deaths/Million')\nfig.show()"
import geopy
from geopy.geocoders import Nominatim
geolocator=Nominatim(user_agent="app")
location = geolocator.geocode("USA")
print(location.latitude)
39.7837304
location.longitude
-100.4458825
latest_data.head()
| Date | Country | Confirmed | Recovered | Deaths | |
|---|---|---|---|---|---|
| 0 | 2020-01-22 | Afghanistan | 0 | 0 | 0 |
| 1 | 2020-01-23 | Afghanistan | 0 | 0 | 0 |
| 2 | 2020-01-24 | Afghanistan | 0 | 0 | 0 |
| 3 | 2020-01-25 | Afghanistan | 0 | 0 | 0 |
| 4 | 2020-01-26 | Afghanistan | 0 | 0 | 0 |
df=latest_data.copy()
df.head()
| Date | Country | Confirmed | Recovered | Deaths | |
|---|---|---|---|---|---|
| 0 | 2020-01-22 | Afghanistan | 0 | 0 | 0 |
| 1 | 2020-01-23 | Afghanistan | 0 | 0 | 0 |
| 2 | 2020-01-24 | Afghanistan | 0 | 0 | 0 |
| 3 | 2020-01-25 | Afghanistan | 0 | 0 | 0 |
| 4 | 2020-01-26 | Afghanistan | 0 | 0 | 0 |
df.shape
(47376, 5)
df[df['Country']=='Afghanistan']
| Date | Country | Confirmed | Recovered | Deaths | |
|---|---|---|---|---|---|
| 0 | 2020-01-22 | Afghanistan | 0 | 0 | 0 |
| 1 | 2020-01-23 | Afghanistan | 0 | 0 | 0 |
| 2 | 2020-01-24 | Afghanistan | 0 | 0 | 0 |
| 3 | 2020-01-25 | Afghanistan | 0 | 0 | 0 |
| 4 | 2020-01-26 | Afghanistan | 0 | 0 | 0 |
| ... | ... | ... | ... | ... | ... |
| 247 | 2020-09-25 | Afghanistan | 39186 | 32619 | 1451 |
| 248 | 2020-09-26 | Afghanistan | 39192 | 32635 | 1453 |
| 249 | 2020-09-27 | Afghanistan | 39227 | 32642 | 1453 |
| 250 | 2020-09-28 | Afghanistan | 39233 | 32642 | 1455 |
| 251 | 2020-09-29 | Afghanistan | 39254 | 32746 | 1458 |
252 rows × 5 columns
df2=df.groupby(['Country'])[['Confirmed','Recovered','Deaths']].max().reset_index()
df2.head()
| Country | Confirmed | Recovered | Deaths | |
|---|---|---|---|---|
| 0 | Afghanistan | 39254 | 32746 | 1458 |
| 1 | Albania | 13518 | 7732 | 384 |
| 2 | Algeria | 51368 | 36063 | 1726 |
| 3 | Andorra | 1966 | 1265 | 53 |
| 4 | Angola | 4905 | 1833 | 179 |
df2[df2['Country']=='India']
| Country | Confirmed | Recovered | Deaths | |
|---|---|---|---|---|
| 79 | India | 6145291 | 5101397 | 96318 |
lat_lon=[]
geolocator=Nominatim(user_agent="app")
for location in df2['Country']:
location = geolocator.geocode(location)
if location is None:
lat_lon.append(np.nan)
else:
geo=(location.latitude,location.longitude)
lat_lon.append(geo)
lat_lon
[(33.7680065, 66.2385139), (41.000028, 19.9999619), (28.0000272, 2.9999825), (42.5407167, 1.5732033), (-11.8775768, 17.5691241), (17.2234721, -61.9554608), (-34.9964963, -64.9672817), (40.7696272, 44.6736646), (-24.7761086, 134.755), (47.2000338, 13.199959), (40.3936294, 47.7872508), (24.7736546, -78.0000547), (26.1551249, 50.5344606), (24.4768783, 90.2932426), (13.1500331, -59.5250305), (53.4250605, 27.6971358), (50.6402809, 4.6667145), (16.8259793, -88.7600927), (9.5293472, 2.2584408), (27.549511, 90.5119273), (-17.0568696, -64.9912286), (44.3053476, 17.5961467), (-23.1681782, 24.5928742), (-10.3333333, -53.2), (4.4137155, 114.5653908), (42.6073975, 25.4856617), (12.0753083, -1.6880314), (17.1750495, 95.9999652), (-3.3634357, 29.8870575), (16.0000552, -24.0083947), (13.5066394, 104.869423), (4.6125522, 13.1535811), (61.0666922, -107.9917071), (7.0323598, 19.9981227), (15.6134137, 19.0156172), (-31.7613365, -71.3187697), (35.000074, 104.999927), (2.8894434, -73.783892), (-12.2045176, 44.2832964), (-0.7264327, 15.6419155), (-2.9814344, 23.8222636), (10.2735633, -84.0739102), (7.9897371, -5.5679458), (45.5643442, 17.0118954), (23.0131338, -80.8328748), (34.9823018, 33.1451285), (49.8167003, 15.4749544), (55.670249, 10.3333283), (53.8953584, 27.5554078), (11.8145966, 42.8453061), (19.0974031, -70.3028026), (19.0974031, -70.3028026), (-1.3397668, -79.3666965), (26.2540493, 29.2675469), (13.8000382, -88.9140683), (1.613172, 10.5170357), (15.9500319, 37.9999668), (58.7523778, 25.3319078), (-26.5624806, 31.3991317), (10.2116702, 38.6521203), (-18.1239696, 179.0122737), (63.2467777, 25.9209164), (46.603354, 1.8883335), (-0.8999695, 11.6899699), (13.470062, -15.4900464), (32.3293809, -83.1137366), (51.0834196, 10.4234469), (8.0300284, -1.0800271), (38.9953683, 21.9877132), (12.1360374, -61.6904045), (15.6356088, -89.8988087), (10.7226226, -10.7083587), (12.100035, -14.9000214), (4.8417097, -58.6416891), (19.1399952, -72.3570972), (38.9247244, -77.06572732690151), (15.2572432, -86.0755145), (47.1817585, 19.5060937), (64.9841821, -18.1059013), (22.3511148, 78.6677428), (-2.4833826, 117.8902853), (32.6475314, 54.5643516), (33.0955793, 44.1749775), (52.865196, -7.9794599), (31.5313113, 34.8667654), (42.6384261, 12.674297), (18.1152958, -77.1598454610168), (36.5748441, 139.2394179), (31.1667049, 36.941628), (47.2286086, 65.2093197), (1.4419683, 38.4313975), (36.638392, 127.6961188), (42.5869578, 20.9021231), (29.2733964, 47.4979476), (41.5089324, 74.724091), (20.0171109, 103.378253), (56.8406494, 24.7537645), (33.8750629, 35.843409), (-29.6039267, 28.3350193), (5.7499721, -9.3658524), (26.8234472, 18.1236723), (47.1416307, 9.5531527), (55.3500003, 23.7499997), (49.8158683, 6.1296751), (52.4387696, 4.8185293), (-18.9249604, 46.4416422), (-13.2687204, 33.9301963), (4.5693754, 102.2656823), (4.7064352, 73.3287853), (16.3700359, -2.2900239), (35.8885993, 14.4476911), (20.2540382, -9.2399263), (-20.2759451, 57.5703566), (19.4326296, -99.1331785), (47.2879608, 28.5670941), (43.7323492, 7.4276832), (46.8250388, 103.8499736), (42.9868853, 19.5180992), (31.1728205, -7.3362482), (-19.302233, 34.9144977), (-23.2335499, 17.3231107), (28.1083929, 84.0917139), (52.5001698, 5.7480821), (-41.5000831, 172.8344077), (12.6090157, -85.2936911), (17.7356214, 9.3238432), (9.6000359, 7.9999721), (41.6171214, 21.7168387), (64.5731537, 11.52803643954819), (21.0000287, 57.0036901), (30.3308401, 71.247499), (8.559559, -81.1308434), (-5.6816069, 144.2489081), (-23.3165935, -58.1693445), (-6.8699697, -75.0458515), (12.7503486, 122.7312101), (52.215933, 19.134422), (40.0332629, -7.8896263), (25.3336984, 51.2295295), (45.9852129, 24.6859225), (64.6863136, 97.7453061), (-1.9646631, 30.0644358), (17.250512, -62.6725973), (13.8250489, -60.975036), (12.90447, -61.2765569), (43.9458623, 12.458306), (0.8875498, 6.9648718), (25.6242618, 42.3528328), (14.4750607, -14.4529612), (44.024322850000004, 21.07657433209902), (-4.6574977, 55.4540146), (8.6400349, -11.8400269), (1.3408630000000001, 103.83039182212079), (48.7411522, 19.4528646), (45.8133113, 14.4808369), (8.3676771, 49.083416), (-28.8166236, 24.991639), (7.8699431, 29.6667897), (39.3262345, -4.8380649), (7.5554942, 80.7137847), (14.5844444, 29.4917691), (4.1413025, -56.0771187), (59.6749712, 14.5208584), (46.7985624, 8.2319736), (34.6401861, 39.0494106), (23.59829785, 120.83536313817521), (38.6281733, 70.8156541), (-6.5247123, 35.7878438), (14.8971921, 100.83273), (-8.5151979, 125.8375756), (8.7800265, 1.0199765), (10.8677845, -60.9821067), (33.8439408, 9.400138), (38.9597594, 34.9249653), (39.7837304, -100.4458825), (1.5333554, 32.2166578), (49.4871968, 31.2718321), (24.0002488, 53.9994829), (54.7023545, -3.2765753), (-32.8755548, -56.0201525), (41.32373, 63.9528098), (8.0018709, -66.1109318), (13.2904027, 108.4265113), (31.4331663, 34.3779285), (24.1797324, -13.7667848), (16.3471243, 47.8915271), (-14.5186239, 27.5599164), (-18.4554963, 29.7468414)]
df2['geo_loc']=lat_lon
#### unzip it
lat,lon=zip(*np.array(df2['geo_loc']))
df2['lat']=lat
df2['lon']=lon
df2.head()
| Country | Confirmed | Recovered | Deaths | geo_loc | lat | lon | |
|---|---|---|---|---|---|---|---|
| 0 | Afghanistan | 39254 | 32746 | 1458 | (33.7680065, 66.2385139) | 33.768006 | 66.238514 |
| 1 | Albania | 13518 | 7732 | 384 | (41.000028, 19.9999619) | 41.000028 | 19.999962 |
| 2 | Algeria | 51368 | 36063 | 1726 | (28.0000272, 2.9999825) | 28.000027 | 2.999983 |
| 3 | Andorra | 1966 | 1265 | 53 | (42.5407167, 1.5732033) | 42.540717 | 1.573203 |
| 4 | Angola | 4905 | 1833 | 179 | (-11.8775768, 17.5691241) | -11.877577 | 17.569124 |
df2.drop(['geo_loc'],axis=1,inplace=True)
df2.head()
| Country | Confirmed | Recovered | Deaths | lat | lon | |
|---|---|---|---|---|---|---|
| 0 | Afghanistan | 39254 | 32746 | 1458 | 33.768006 | 66.238514 |
| 1 | Albania | 13518 | 7732 | 384 | 41.000028 | 19.999962 |
| 2 | Algeria | 51368 | 36063 | 1726 | 28.000027 | 2.999983 |
| 3 | Andorra | 1966 | 1265 | 53 | 42.540717 | 1.573203 |
| 4 | Angola | 4905 | 1833 | 179 | -11.877577 | 17.569124 |
df2.to_csv('F:/Spatial Analysis/Spatial_data.csv')
Plotting Markers on the Map Folium gives a folium.Marker() class for plotting markers on a map Just pass the latitude and longitude of the location, mention the popup and tooltip and add it to the map.
1) you need to create a base map on which your markers will be placed
2) and then add your markers to it:
import folium
folium.Map(tiles='openstreetmap', zoom_start=2)
# Create a map
m = folium.Map(location=[54, 15], tiles='openstreetmap', zoom_start=2)
# Add points to the map
for id,row in df2.iterrows():
folium.Marker(location=[row['lat'],row['lon']], popup=row['Confirmed']).add_to(m)
# Display the map
m
m = folium.Map(location=[54, 15], tiles='openstreetmap', zoom_start=2)
# Add points to the map
for idx, row in df2.iterrows():
folium.Marker([row['lat'], row['lon']], popup=row['Recovered']).add_to(m)
# Display the map
m
m = folium.Map(location=[54, 15], tiles='openstreetmap', zoom_start=2)
# Add points to the map
for idx, row in df2.iterrows():
folium.Marker([row['lat'], row['lon']], popup=row['Deaths']).add_to(m)
# Display the map
m
m = folium.Map(location=[54,15], tiles='cartodbpositron', zoom_start=2)
# Add points to the map
from folium.plugins import MarkerCluster
mc = MarkerCluster()
for idx, row in df2.iterrows():
mc.add_child(folium.Marker([row['lat'], row['lon']],popup=row['Confirmed']))
m.add_child(mc)
# Display the map
m
from folium.plugins import HeatMap
df2.head()
| Country | Confirmed | Recovered | Deaths | lat | lon | |
|---|---|---|---|---|---|---|
| 0 | Afghanistan | 39254 | 32746 | 1458 | 33.768006 | 66.238514 |
| 1 | Albania | 13518 | 7732 | 384 | 41.000028 | 19.999962 |
| 2 | Algeria | 51368 | 36063 | 1726 | 28.000027 | 2.999983 |
| 3 | Andorra | 1966 | 1265 | 53 | 42.540717 | 1.573203 |
| 4 | Angola | 4905 | 1833 | 179 | -11.877577 | 17.569124 |
# Create map with overall cases registered
m = folium.Map(location=[54,15], zoom_start=2)
HeatMap(data=df2[['lat', 'lon','Confirmed']], radius=15).add_to(m)
# Show the map
m